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RAGLight MCP Server

Bessouat40/RAGLight

Score: 61.7 Rank #5816 Agent Framework
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RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connect external tools and data sources.

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Overview

Bessouat40/RAGLight is a Python tool licensed under MIT. RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connect external tools and data sources. Topics: data-science, framework, huggingface, ollama, retrieval-augmented-generation, vector-database, artificial-intelligence, rag, lmstudio, retrieval-augmented, agentic-rag, mistral-api, mistralai, agentic-ai, agentic-workflow, openai, openai-api, mcp, mcp-tools.

Ranked #5816 out of 24846 indexed tools.

Ecosystem

Python MIT
data-scienceframeworkhuggingfaceollamaretrieval-augmented-generationvector-databaseartificial-intelligenceraglmstudioretrieval-augmentedagentic-ragmistral-apimistralaiagentic-aiagentic-workflowopenaiopenai-apimcpmcp-tools

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 672

672 stars → growing community

Freshness 25% 2mo ago

Last commit 2mo ago → moderate activity

Issue Health 25% 58%

30/52 issues closed → decent issue management

Contributors 10% 5

5 contributors → small team

Dependents 25% 6

6 dependents → few downstream users

npm Downloads N/A
PyPI Downloads N/A
Forks 104
Description Detailed
License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Freshness high impact

Last commit was 70 days ago — a recent commit would boost your freshness score

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Matched Queries

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From the README

# RAGLight

<div align="center">
    
</div>

**RAGLight** is a lightweight and modular Python library for implementing **Retrieval-Augmented Generation (RAG)**. It enhances the capabilities of Large Language Models (LLMs) by combining document retrieval with natural language inference.

Designed for simplicity and flexibility, RAGLight provides modular components to easily integrate various LLMs, embeddings, and vector stores, making it an ideal tool for building context-aware AI solutions.

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## 📚 Table of Contents

- [Requirements](#⚠️-requirements)

- [Features](#features)

- [Import library](#import-library-🛠️)

- [Chat with Your Documents Instantly With CLI](#chat-with-your-documents-instantly-with-cli-💬)
  - [Ignore Folders Feature](#ignore-folders-feature-🚫)
  - [Ignore Folders in Configuration Classes](#ignore-folders-in-configuration-classes-🚫)

- [Deploy as a REST API (raglight serve)](#deploy-as-a-rest-api-raglight-serve-🌐)
  - [Start the server](#start-the-server)
Read full README on GitHub →

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